Knowing NumPy's internals is crucially important when you are working with scientific operations. Efficiency is key since many scientific computations are compute and memory intensive. Hence, if your code is not written efficiently, computations will take much longer than they need and this will hurt your research and development timeline.
In this chapter, you have seen some of the internals and performance aspects of the NumPy library and also learned about the vprof library, which helps you inspect the performance of your python programs.
Code profiling will help you a lot to inspect your programs line by line and there are different ways of looking at the same data, as you have seen previously. Once you have identified the most demanding parts of your programs, then you can start searching for more efficient ways or implementations to improve performance and save...
